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Decision Analysis with Geographically Varying Outcomes: Preference Models and Illustrative Applications

Author

Listed:
  • Jay Simon

    (Defense Resources Management Institute, Naval Postgraduate School, Monterey, California 93943)

  • Craig W. Kirkwood

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

  • L. Robin Keller

    (The Paul Merage School of Business, University of California, Irvine, California 92697)

Abstract

This paper presents decision analysis methodology for decisions based on data from geographic information systems. The consequences of a decision alternative are modeled as distributions of outcomes across a geographic region. We discuss conditions that may conform with the decision maker's preferences over a specified set of alternatives; then we present specific forms for value or utility functions that are implied by these conditions. Decisions in which there is certainty about the consequences resulting from each alternative are considered first; then probabilistic uncertainty about the consequences is included as an extension. The methodology is applied to two hypothetical urban planning decisions involving water use and temperature reduction in regional urban development, and fire coverage across a city. These examples illustrate the applicability of the approach and the insights that can be gained from using it.

Suggested Citation

  • Jay Simon & Craig W. Kirkwood & L. Robin Keller, 2014. "Decision Analysis with Geographically Varying Outcomes: Preference Models and Illustrative Applications," Operations Research, INFORMS, vol. 62(1), pages 182-194, February.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:1:p:182-194
    DOI: 10.1287/opre.2013.1217
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    References listed on IDEAS

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    Cited by:

    1. Andrea C. Hupman & Jay Simon, 2023. "The Legacy of Peter Fishburn: Foundational Work and Lasting Impact," Decision Analysis, INFORMS, vol. 20(1), pages 1-15, March.
    2. Jay Simon, 2020. "Weight Approximation for Spatial Outcomes," Sustainability, MDPI, vol. 12(14), pages 1-18, July.
    3. L. Robin Keller & Jay Simon, 2019. "Preference Functions for Spatial Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 244-256, January.
    4. Harju, Mikko & Liesiö, Juuso & Virtanen, Kai, 2019. "Spatial multi-attribute decision analysis: Axiomatic foundations and incomplete preference information," European Journal of Operational Research, Elsevier, vol. 275(1), pages 167-181.
    5. Jay Simon, 2016. "On the existence of altruistic value and utility functions," Theory and Decision, Springer, vol. 81(3), pages 371-391, September.
    6. Bana e Costa, Carlos A. & Oliveira, Mónica D. & Rodrigues, Teresa C. & Vieira, Ana C.L., 2023. "Desirability–doability group judgment framework for the collaborative multicriteria evaluation of public policies," LSE Research Online Documents on Economics 118192, London School of Economics and Political Science, LSE Library.
    7. Bana e Costa, Carlos A. & Oliveira, Mónica D. & Vieira, Ana C.L. & Freitas, Liliana & Rodrigues, Teresa C. & Bana e Costa, João & Freitas, Ângela & Santana, Paula, 2023. "Collaborative development of composite indices from qualitative value judgements: The EURO-HEALTHY Population Health Index model," European Journal of Operational Research, Elsevier, vol. 305(1), pages 475-492.

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